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Decision noise in reward-guided learning amidst option unavailability

Navigating a new city's dining scene often confronts us with unavailable options, e.g., 'fully booked,' shaping our behavioral variability such as random exploratory choices and learning errors. Existing theories of reward-guided... ver más

30/09/2026

ENS

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ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-10-01

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

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Duración del proyecto: 23 meses Fecha Inicio: 2024-10-01
Fecha Fin: 2026-09-30

Líder del proyecto

ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Navigating a new city's dining scene often confronts us with unavailable options, e.g., 'fully booked,' shaping our behavioral variability such as random exploratory choices and learning errors. Existing theories of reward-guided learning typically overlook option unavailability. Hypothesized strategies may vary, from adjusting action stochasticity—over-exploration to broadly check alternatives, or under-exploration to quickly latch onto available favorites—to adjusting learning process, e.g., weighing past choices for maximal future rewards, or heuristically inflating the value of busy venues while devaluating others ('busier = better'). A nuanced understanding of these tactics requires a deep dive into information sampling, encompassing not just overt evidence-seeking actions but also covert attentional sampling during value updates in the internal model of the world. These complex sampling patterns, unfolding rapidly over hundreds of milliseconds, have been chiefly studied through eye-tracking that captures overt oculomotor variables, leaving the subtleties of covert sampling unexplored and detailed mechanistic insights elusive. I aim to address this issue across multiple scales—from neurotransmitter and neural dynamics to biophysically-grounded modeling of behavior. Using pharmaco-magnetoencephalography (MEG), I will track covert attentional focus across the entire timespan of every single reward-guided decision during learning, in detailed source-localized brain regions, while also perturbing neurochemical systems to establish causal links. This research promises to illuminate longstanding questions, such as why behavioral variability persists despite evolutionary pressures favoring reward-maximizing decisions. The training through this MSCA in circuit-level mechanistic models, imparted by world-leading experts, will complement my existing expertise, and establish me as an independent researcher in the burgeoning field of cognitive computational neuroscience.

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